Why Your LLM Forgets Your Code After 10 Prompts (And How to Fix Context Drift)

We’ve all been there. You’re deep in the zone, building out a complex feature. You open up your favorite LLM (ChatGPT, Claude, whatever you're using locally) to act as your rubber duck and copilot. Your initial prompts are gold. The AI perfectly grasps the nuances of your Next.js architecture or your messy database schema. You go back and forth, iterating, refactoring, and refining the details. But right around prompt #15, something shifts. The AI’s code suggestions become slightly generic. It imports a library you explicitly told it not to use. By prompt #20, you read the output and realize the AI has completely forgotten the entire premise of your project. It feels like you are pair-programming with someone who just woke up from a nap. In the AI engineering space, this isn’t just a random API hiccup. According to AI Engineer Chandra Sekhar, this is a highly predictable failure mode known as a Context Drift Hallucination . If you are building AI wrappers, internal developer tools, or